This a template for an analysis notebook using RMarkdown.
In this notebook, we will set up parameters in the seurat workflow [normalization –> reduction –> clustering] for one Wilms tumor sample (SCPCS000169) of the Wilms Tumor dataset (SCPCP000006).
This correspond to the step 2 of the proposed analysis: clustering of cells across a set of parameters for few samples
In order to assess the clustering quality, we look into some marker genes, pathways enrichment and label transfer.
## to be modified when rendered
filedir <- filelist[i]
sample <- gsub(paste0(data_dir, "/"), "", filedir)
metadata <- read.table(file.path(data_dir, "/single_cell_metadata.tsv"), sep = "\t", header=TRUE)
metadata[metadata$scpca_sample_id == sample,]| scpca_project_id | scpca_sample_id | scpca_library_id | diagnosis | subdiagnosis | disease_timing | age_at_diagnosis | sex | tissue_location | participant_id | submitter | submitter_id | organism | development_stage_ontology_term_id | sex_ontology_term_id | organism_ontology_id | self_reported_ethnicity_ontology_term_id | disease_ontology_term_id | tissue_ontology_term_id | metastasis | relapse_status | treatment | vital_status | seq_unit | technology | total_reads | mapped_reads | sample_cell_count_estimate | unfiltered_cells | filtered_cell_count | processed_cells | has_cellhash | includes_anndata | is_cell_line | is_multiplexed | is_xenograft | pi_name | project_title | genome_assembly | mapping_index | alevin_fry_version | salmon_version | transcript_type | droplet_filtering_method | cell_filtering_method | prob_compromised_cutoff | min_gene_cutoff | normalization_method | date_processed | workflow | workflow_version | workflow_commit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | SCPCP000006 | SCPCS000170 | SCPCL000207 | Wilms tumor | Favorable | Initial diagnosis | 3.5 | F | Kidney | SJWLM071556 | murphy_chen | SJWLM071556_D1 | Homo sapiens | HsapDv:0000097 | PATO:0000383 | NCBITaxon:9606 | unknown | MONDO:0006058 | UBERON:0002113 | NA | No | Upfront resection | Alive | nucleus | 10Xv3.1 | 194036441 | 136641781 | 33536 | 72320 | 33536 | 26532 | False | True | False | False | False | murphy_chen | Single nuclear RNA-seq and spatial transcriptomic analysis of anaplastic and favorable histology Wilms tumor | Homo_sapiens.GRCh38.104 | Homo_sapiens.GRCh38.104.spliced_intron.txome | 0.7.0 | 1.5.2 | [total, spliced] | emptyDropsCellRanger | miQC | 0.75 | 200 | deconvolution | 2024-03-18T20:20:15+0000 | https://github.com/AlexsLemonade/scpca-nf | v0.8.0 | 8bef82d853d19e5aeddd75401aa54cf8bfbced13 |
We perform the following analysis to assess for the quality of clustering:
[1] We perform some quality check to assess any QC-induced clustering (nFeature, nCount, percent.mito).
[2] We add cell cycle information, as we know that in a specific cell cycle state, the transcriptional program is mostly/exclusively related to cell cycle genes and the identity of cells is difficult to determine. We expect these cells to cluster together in a cluster of proliferating cells.
[3] We look at specific marker genes that we reported in the table marker.sets/CellType_metadata.csv to check the relevance of the clustering.
[4] We look at specific pathways that we reported in the table marker.sets/Pathways_metadata.csv to check the relevance of the clustering.
[5] We run DElegate::FindAllMarkers2 to find markers of the different clusters and manually check if they do make sense. DElegate::FindAllMarkers2 is an improved version of Seurat::FindAllMarkers based on pseudobulk differential expression method.
[6] We perform enrichment analysis of marker genes for each seurat clusters. We defined all the genes from the seurat object as the universe and used the MSigDB gene sets.
[7] We plot pca/umap reduction grouping with available annotations (singler_, cellassign_). We expect at least immune cells to be correctly label and fall into a few set of clusters.
[ ] Next step, aim for a next PR: We will label transfer (Azimuth) to transfer annotation from the fetal kidney atlas human reference. We plot pca/umap reduction grouping with latest labels. We expect it to be the most representative of the cell types in the sample.
Please note: to keep the notebook as straight as possible, we decided to show the analysis for the selected set of parameters:
Note: Other parameters have been previously tested, but we would like to show in the following report that the one selected is performing good.
# convert to seurat
srat <- CreateSeuratObject(counts = counts(sce),
assay = "RNA",
project = sample
)
# convert colData and rowData to data.frame for use in the Seurat object
cell_metadata <- as.data.frame(colData(sce))
row_metadata <- as.data.frame(rowData(sce))
# add cell metadata (colData) from SingleCellExperiment to Seurat
srat@meta.data <- cell_metadata
# add row metadata (rowData) from SingleCellExperiment to Seurat
srat[["RNA"]]@meta.data <- row_metadata
# add metadata from SingleCellExperiment to Seurat
srat@misc <- metadata(sce)
# Normalization
options(future.globals.maxSize= 8912896000000)
srat <- SCTransform(srat, verbose = F, conserve.memory = TRUE)
# dimensionality reduction
srat <- RunPCA(srat, verbose = F)
srat <- RunUMAP(srat, dims = 1:50, verbose = F)
# clustering
srat <- FindNeighbors(srat, dims = 1:50, verbose = F)
srat <- FindClusters(srat, verbose = T)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 26532
## Number of edges: 821246
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8020
## Number of communities: 14
## Elapsed time: 4 seconds
d2 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - umap")
d1 <- SCpubr::do_DimPlot(srat, reduction="pca", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - pca")
v <- SCpubr::do_ViolinPlot(srat, features = c( "subsets_mito_percent"), ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(2,2,4))We expect up to 5 set of clusters:
s.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$s.genes]
g2m.genes <- srat@assays$RNA@meta.data$gene_ids[srat@assays$RNA@meta.data$gene_symbol %in% cc.genes$g2m.genes]
srat <- CellCycleScoring(srat, s.features = s.genes, g2m.features = g2m.genes, set.ident = FALSE)d2 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "Phase", label = TRUE) + ggtitle("Phase - umap")
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE) + ggtitle("Seurat Cluster - umap")
b <- SCpubr::do_BarPlot(sample = srat,
group.by = "Phase",
split.by = "seurat_clusters",
position = "fill",
font.size = 10,
legend.ncol = 3) +
ggtitle("% cells")+
xlab(file)
d1 + d2 + b + plot_layout(ncol = 3, widths = c(2,2,4)) SCpubr::do_ViolinPlot(srat, features = c("S.Score", "G2M.Score"), ncol = 2, group.by = "seurat_clusters", legend.position = "none") Here, we open the table of marker genes marker-sets/CellType_metadata.csv. Note: we do not expect to have a clear and nice pattern of expression for all of the following markers in every tumor. This is just ti get a few idea.
DT::datatable(CellType_metadata, caption = ("CellType_metadata"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel'))) SCpubr::do_ViolinPlot(srat, features = rownames(srat)[rownames(srat) %in% (CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class %in% c("malignant")])] , ncol = 9, group.by = "seurat_clusters", legend.position = "none") d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000149294", pt.size = 0.2) + ggtitle("NCAM1")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000149294", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000163359", pt.size = 0.2) + ggtitle("COL6A3")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000163359", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000167244", pt.size = 0.2) + ggtitle("IGF2")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000167244", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000184937", pt.size = 0.2) + ggtitle("WT1")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000184937", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000119888", pt.size = 0.2) + ggtitle("EPCAM")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000119888", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3)) SCpubr::do_ViolinPlot(srat, features = rownames(srat)[rownames(srat) %in% (CellType_metadata$ENSEMBL_ID[CellType_metadata$cell_class %in% c("immune")])]
, ncol = 6, group.by = "seurat_clusters", legend.position = "none") d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000081237", pt.size = 0.2) + ggtitle("PTPRC = CD45")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000081237", ncol = 1, group.by = "seurat_clusters", legend.position = "none", pt.size = 2)
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3)) SCpubr::do_ViolinPlot(srat, features = rownames(srat)[rownames(srat) %in% (CellType_metadata$ENSEMBL_ID[! CellType_metadata$cell_class %in% c("immune", "malignant")])]
, ncol = 6, group.by = "seurat_clusters", legend.position = "none") d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "ENSG00000110799", pt.size = 0.2) + ggtitle("VWF")
v <- SCpubr::do_ViolinPlot(srat, features = "ENSG00000110799", ncol = 1, group.by = "seurat_clusters", legend.position = "none", pt.size = 2)
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))Here, we open the table of marker genes marker-sets/Pathway_metadata.csv.
DT::datatable(Pathway_metadata, caption = ("Pathway_metadata"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))here we will calculate a TP53 score using AddMduleScore and the genes of the HALLMARK_P53_PATHWAY gene set.
## define genesets
hallmarks <- msigdbr(species = "human", category = "H")
TP53_list = hallmarks %>%
filter(gs_name == "HALLMARK_P53_PATHWAY") %>%
dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()
TP53_list <- list(TP53_list$human_ensembl_gene)
srat <- AddModuleScore(srat, features = TP53_list, name = "TP53_score")## Warning: The following features are not present in the object: ENSG00000147889, ENSG00000147883, ENSG00000137975, ENSG00000284841, ENSG00000276536,
## ENSG00000206377, ENSG00000206478, ENSG00000227231, ENSG00000230128, ENSG00000235030, ENSG00000237155, ENSG00000163083, ENSG00000128422, ENSG00000177551,
## ENSG00000175793, ENSG00000206297, ENSG00000224212, ENSG00000224748, ENSG00000226173, ENSG00000227816, ENSG00000230705, ENSG00000232367, ENSG00000125657, not
## searching for symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "TP53_score1", pt.size = 0.2) + ggtitle("TP53 score")
v <- SCpubr::do_ViolinPlot(srat, features = "TP53_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))“Normal” cells (immune, endothelial) have a slightly higher TP53 score.
here we will calculate a DNA_repair score using AddMduleScore and the genes of the HALLMARK_DNA_REPAIR gene set.
DNA_repair = hallmarks %>%
filter(gs_name == "HALLMARK_DNA_REPAIR") %>%
dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()
DNA_repair_list <- list(DNA_repair$human_ensembl_gene)
srat <- AddModuleScore(srat, features = DNA_repair_list, name = "DNA_repair_score")## Warning: The following features are not present in the object: ENSG00000284752, ENSG00000288114, ENSG00000206268, ENSG00000206357, ENSG00000229363,
## ENSG00000231044, ENSG00000233801, ENSG00000180099, ENSG00000206502, ENSG00000224859, ENSG00000233795, ENSG00000235176, ENSG00000235443, ENSG00000236808,
## ENSG00000236949, ENSG00000284832, ENSG00000280627, ENSG00000276463, ENSG00000285339, ENSG00000274352, not searching for symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "DNA_repair_score1", pt.size = 0.2) + ggtitle("DNA_repair score")
v <- SCpubr::do_ViolinPlot(srat, features = "DNA_repair_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))Note: Chemo-treated samples should have higher DNA-damage scores.
## define genesets
c3 <- msigdbr(species = "human", category = "C3", subcategory = "TFT:GTRD")
DROSHA_list = c3 %>%
filter(gs_name == "DROSHA_TARGET_GENES") %>%
dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()
DROSHA_list <- list(DROSHA_list$human_ensembl_gene)
srat <- AddModuleScore(srat, features = DROSHA_list, name = "DROSHA_score")## Warning: The following features are not present in the object: ENSG00000265134, ENSG00000210164, ENSG00000210100, ENSG00000210156, ENSG00000209082,
## ENSG00000210112, ENSG00000210107, ENSG00000210151, ENSG00000210077, ENSG00000248923, ENSG00000199568, ENSG00000207205, ENSG00000275538, not searching for
## symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "DROSHA_score1", pt.size = 0.2) + ggtitle("DROSHA score")
v <- SCpubr::do_ViolinPlot(srat, features = "DROSHA_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))DICER1_list = c3 %>%
filter(gs_name == "DICER1_TARGET_GENES") %>%
dplyr::distinct(gs_name, gene_symbol, human_ensembl_gene) %>% as.data.frame()
DICER1_list <- list(DICER1_list$human_ensembl_gene)
srat <- AddModuleScore(srat, features = DICER1_list, name = "DICER1_score")## Warning: The following features are not present in the object: ENSG00000210049, ENSG00000210100, ENSG00000210156, ENSG00000210196, ENSG00000210151,
## ENSG00000210195, ENSG00000222328, ENSG00000199347, not searching for symbol synonyms
d1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
d2 <- SCpubr::do_FeaturePlot(srat, features = "DICER1_score1", pt.size = 0.2) + ggtitle("DICER1 score")
v <- SCpubr::do_ViolinPlot(srat, features = "DICER1_score1", ncol = 1, group.by = "seurat_clusters", legend.position = "none")
d1 + d2 + v + plot_layout(ncol = 3, widths = c(1,1,3))In addition to the list of known marker genes, we used an unbiased approach to find transcripts that characterized the different clusters. We run DElegate::FindAllMarkers2 to find markers of the different clusters and manually check if they do make sense. DElegate::FindAllMarkers2 is an improved version of Seurat::FindAllMarkers based on pseudobulk differential expression method. Please check the preprint from Chistoph Hafemeister: https://www.biorxiv.org/content/10.1101/2023.03.28.534443v1 and tool described here: https://github.com/cancerbits/DElegate
feature_conversion <- srat@assays$RNA@meta.data
de_results <- DElegate::FindAllMarkers2(srat, group_column = "seurat_clusters")
#filter the most relevant markers
s.markers <- de_results[de_results$padj < cfg$padj_thershold & de_results$log_fc > cfg$lfc_threshold & de_results$rate1 > cfg$rate1_threshold,]
# add gene symbol for easiest interpretation of the result
s.markers$gene_ids <- s.markers$feature
s.markers <- left_join(s.markers,feature_conversion, by = c( "gene_ids") )
identical(s.markers$feature, s.markers$gene_ids) # check the quality of the merge, must be true## [1] TRUE
DT::datatable(s.markers, caption = ("marker genes"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))# Select top 5 genes for heatmap plotting
s.markers <- na.omit(s.markers)
s.markers %>%
group_by(group1) %>%
top_n(n = 5, wt = log_fc) -> top5
# subset for plotting
cells <- WhichCells(srat, downsample = 100)
ss <- subset(srat, cells = cells)
ss <- ScaleData(ss, features = top5$feature)
p1 <- SCpubr::do_DimPlot(srat, reduction="umap", group.by = "seurat_clusters", label = TRUE, repel = TRUE) + ggtitle("Seurat Cluster - umap")
p2 <- DoHeatmap(ss, features = top5$feature, cells = cells, group.by = "seurat_clusters") + NoLegend() +
scale_fill_gradientn(colors = c("#01665e","#35978f",'darkslategray3', "#f7f7f7", "#fee391","#fec44f","#F9AD03"))
p3 <- ggplot(srat@meta.data, aes(seurat_clusters, fill = seurat_clusters)) + geom_bar() + NoLegend()
common_title <- sprintf("Unsupervised clustering %s, %d cells", srat@meta.data$orig.ident[1], ncol(srat))
show((((p1 / p3) + plot_layout(heights = c(3,2)) | p2) ) + plot_layout(widths = c(1, 2)) + plot_layout(heights = c(3,1)) + plot_annotation(title = common_title))Here we perform enrichment analysis of the marker genes found in the previous section for each Seurat cluster.
We defined as universe/background all the genes expressed in the dataset, meaning the rownames of the Seurat object.
We used three gene sets from MSiGDB:
We used enricher function from clusterProfiler to perform enrichment analysis.
# define background genes = universe for enrichment
background <- row_metadata$gene_ids
## define genesets
c8 <- msigdbr(species = "human", category = "C8")
msigdbr_hallmarks = hallmarks %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()
msigdbr_c3 = c3 %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()
msigdbr_c8 = c8 %>% dplyr::distinct(gs_name, ensembl_gene) %>% as.data.frame()tmp_H <- NULL
for(i in unique(srat$seurat_clusters)){
signature = s.markers$feature[s.markers$group1 == i]
ego_module <- enricher(gene = signature, universe = background, TERM2GENE = msigdbr_hallmarks)
tmp <- NULL
if(!is.null(ego_module)){
if(dim(ego_module)[1]>0){
b_H <- barplot(ego_module, showCategory = 15, font.size = 20, label_format = 40)
tmp <- b_H$data
tmp$set <- "Hallmarks"
tmp$cluster <- i
}
}
tmp_H <- rbind(tmp_H, tmp)
}
ggplot(tmp_H, aes(Count, ID)) +
geom_bar(stat = "identity", aes(fill=cluster))+
theme_classic()+
geom_text(
aes(label = (paste( "Padj = ", round(p.adjust,2)))),
color = "black",
size = 3,
hjust=1,
position = position_dodge(0.5)
)+
theme(text = element_text(size=14))+
facet_wrap(facets=c("cluster"), ncol = length(unique(tmp_H$cluster)))EMT signature should be enriched in stroma cluster. E2F/proliferation should be enriched in blastema cluster. MYC(N), TP53 must be enriched in blastema cluster.
tmp_H <- NULL
for(i in unique(srat$seurat_clusters)){
signature = s.markers$feature[s.markers$group1 == i]
ego_module <- enricher(gene = signature, universe = background, TERM2GENE = msigdbr_c3)
tmp <- NULL
if(!is.null(ego_module)){
if(dim(ego_module)[1]>0){
b_H <- barplot(ego_module, showCategory = 15, font.size = 20, label_format = 40)
tmp <- b_H$data
tmp$ID <- factor(tmp$ID, levels = c(tmp$ID[order(tmp$Count)]))
tmp$set <- "c8"
tmp$cluster <- i
}
}
tmp_H <- rbind(tmp_H, tmp[1:5,])
}
ggplot(tmp_H, aes(Count, ID)) +
geom_bar(stat = "identity", aes(fill=cluster))+
theme_classic()+
geom_text(
aes(label = (paste( "Padj = ", round(p.adjust,2)))),
color = "black",
size = 3,
hjust=1,
position = position_dodge(0.5)
)+
theme(text = element_text(size=14))+
facet_wrap(facets=c("cluster"), ncol = length(unique(tmp_H$cluster)))## Warning: Removed 7 rows containing missing values or values outside the scale range (`geom_bar()`).
## Warning: Removed 7 rows containing missing values or values outside the scale range (`geom_text()`).
Here to check of we catch and MIR pattern (DROSHA; DICER1; other?)
tmp_H <- NULL
for(i in unique(srat$seurat_clusters)){
signature = s.markers$feature[s.markers$group1 == i]
ego_module <- enricher(gene = signature, universe = background, TERM2GENE = msigdbr_c8)
tmp <- NULL
if(!is.null(ego_module)){
if(dim(ego_module)[1]>0){
b_H <- barplot(ego_module, showCategory = 15, font.size = 20, label_format = 40)
tmp <- b_H$data
tmp$ID <- factor(tmp$ID, levels = c(tmp$ID[order(tmp$Count)]))
tmp$set <- "c8"
tmp$cluster <- i
}
}
tmp_H <- rbind(tmp_H, tmp[1:5,])
}
ggplot(tmp_H, aes(Count, ID)) +
geom_bar(stat = "identity", aes(fill=cluster))+
theme_classic()+
geom_text(
aes(label = (paste( "Padj = ", round(p.adjust,2)))),
color = "black",
size = 3,
hjust=1,
position = position_dodge(0.5)
)+
theme(text = element_text(size=14))+
facet_wrap(facets=c("cluster"), ncol = length(unique(tmp_H$cluster)))## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_bar()`).
## Warning: Removed 3 rows containing missing values or values outside the scale range (`geom_text()`).
The MSigDB C8 gene set is quite relevant for kidney and nephroblastoma annotations. Epithelial (cancer and normal) cells should be enriched in mature/adulte kidney pathways while blastema cancer cells will show enrichment of fetal kidney development pathway / cap mesenchyme.
Here, we quickly checked annotations that are present in the _processed rds object. However, the automated annotation have not been performed using a cancer specific reference or a kidney reference. We do not expect a nice labelling of the cells as the overlap of cell types between the reference and the query dataset is poor. This support the need to do a proper label transfer from the fetal kidney atlas, which is imho the best reference that can be applied to a Wilms tumor query.
d2 <-DimPlot(srat, group.by = "singler_celltype_annotation", reduction = "umap", label = TRUE, repel = TRUE) + NoLegend()
DT::datatable(table(srat$seurat_clusters, srat$singler_celltype_annotation), caption = ("table of SingleR annotation per seurat clusters"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))d3 <- SCpubr::do_BarPlot(sample = srat,
group.by = "singler_celltype_annotation",
split.by = "seurat_clusters",
position = "fill",
font.size = 10,
legend.ncol = 3) +
ggtitle("% cells")+
xlab(file)
d2|d3## Warning: ggrepel: 13 unlabeled data points (too many overlaps). Consider increasing max.overlaps
d2 <-DimPlot(srat, group.by = "cellassign_celltype_annotation", reduction = "umap", label = TRUE, repel = TRUE) + NoLegend()
DT::datatable(table(srat$seurat_clusters, srat$cellassign_celltype_annotation)
, caption = ("table of cellassign annotation per seurat clusters"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))d3 <- SCpubr::do_BarPlot(sample = srat,
group.by = "cellassign_celltype_annotation",
split.by = "seurat_clusters",
position = "fill",
font.size = 10,
legend.ncol = 3) +
ggtitle("% cells")+
xlab(file)
d2|d3Even if the result of SingleR and CellAssign are not specific to a Wilms tumor dataset, it give the first impression that C12 is a cluster of endothelial cells and C11 a cluster of immune cells.
To be added in a next PR.
For more information related to the reference, please go to https://www.kidneycellatlas.org/ You will find:
interactive viewer
h5ad files to download.
Please note that as Wilms tumor have been described to be closer to fetal kidney as mature kidney, we only used the fetal kidney atlas as the reference. Also check : https://www.science.org/doi/10.1126/science.aat5031
This part is imho one of the most important step that allow us to have a quick and reliable idea of the composition of the different clusters. The predicted compartment are defined into 4 categories:
As for SingleR and CellAssign, the annotation of immune cells and endothelial cells is straightforward. The stroma compartment should then contain normal and cancer stromal cells. The fetal nephron compartment contain blastema cancer cells a well as normal and cancer epithelial cells.
Further segregation of cancer versus normal cells will be achieved using a combination of markers/pathways (see above) and inferred CNV (to be done).
-[x] We will adapt this template to be render on all the 40 Wilms tumor samples of the dataset.
-[ ] We will save for each sample the rds file
-[ ] We will run inferCNV for each sample to decide the malignant/normal status of some stroma and epithelial cluster, and confirm the blastema annotation.
The next step will provide us a better understanding of the entire cohort. We will then have to set up a strategy to annotate each sample. Open questions are:
[ ] should we annotate single cell
or
[] consider applying similar annotations to all cells in a cluster?
[ ] manual annotation of each cluster / each patient
or
[] automated annotation using some threshold?
# record the versions of the packages used in this analysis and other environment information
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
## [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Vienna
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] assertthat_0.2.1 data.table_1.15.4 clusterProfiler_4.12.2 msigdbr_7.5.1 DElegate_1.2.1
## [6] edgeR_4.2.1 limma_3.60.4 viridis_0.6.5 viridisLite_0.4.2 SCpubr_2.0.2
## [11] ggplotify_0.1.2 patchwork_1.2.0 ggplot2_3.5.1 DT_0.33 dplyr_1.1.4
## [16] tidyr_1.3.1 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0 GenomicRanges_1.56.1
## [21] GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1 BiocGenerics_0.50.0 MatrixGenerics_1.16.0
## [26] matrixStats_1.3.0 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4 reticulate_1.38.0
## [31] Azimuth_0.5.0 shinyBS_0.61.1
##
## loaded via a namespace (and not attached):
## [1] R.methodsS3_1.8.2 poweRlaw_0.80.0 goftest_1.2-3 Biostrings_2.72.1
## [5] vctrs_0.6.5 spatstat.random_3.3-1 digest_0.6.36 png_0.1-8
## [9] ggrepel_0.9.5 deldir_2.0-4 parallelly_1.38.0 renv_1.0.7
## [13] MASS_7.3-61 Signac_1.13.0 reshape2_1.4.4 httpuv_1.6.15
## [17] qvalue_2.36.0 withr_3.0.1 xfun_0.46 ggfun_0.1.5
## [21] survival_3.7-0 EnsDb.Hsapiens.v86_2.99.0 memoise_2.0.1 gson_0.1.0
## [25] tidytree_0.4.6 zoo_1.8-12 gtools_3.9.5 pbapply_1.7-2
## [29] R.oo_1.26.0 KEGGREST_1.44.1 promises_1.3.0 httr_1.4.7
## [33] restfulr_0.0.15 globals_0.16.3 fitdistrplus_1.2-1 rhdf5filters_1.16.0
## [37] rhdf5_2.48.0 rstudioapi_0.16.0 UCSC.utils_1.0.0 miniUI_0.1.1.1
## [41] generics_0.1.3 DOSE_3.30.2 babelgene_22.9 curl_5.2.1
## [45] zlibbioc_1.50.0 ggraph_2.2.1 polyclip_1.10-7 GenomeInfoDbData_1.2.12
## [49] SparseArray_1.4.8 xtable_1.8-4 stringr_1.5.1 pracma_2.4.4
## [53] evaluate_0.24.0 S4Arrays_1.4.1 hms_1.1.3 irlba_2.3.5.1
## [57] colorspace_2.1-1 hdf5r_1.3.11 ROCR_1.0-11 spatstat.data_3.1-2
## [61] magrittr_2.0.3 lmtest_0.9-40 readr_2.1.5 glmGamPoi_1.16.0
## [65] later_1.3.2 ggtree_3.12.0 lattice_0.22-6 spatstat.geom_3.3-2
## [69] future.apply_1.11.2 shadowtext_0.1.4 scattermore_1.2 XML_3.99-0.17
## [73] cowplot_1.1.3 RcppAnnoy_0.0.22 pillar_1.9.0 nlme_3.1-165
## [77] pwalign_1.0.0 caTools_1.18.2 compiler_4.4.1 RSpectra_0.16-2
## [81] stringi_1.8.4 tensor_1.5 GenomicAlignments_1.40.0 plyr_1.8.9
## [85] crayon_1.5.3 abind_1.4-5 BiocIO_1.14.0 gridGraphics_0.5-1
## [89] googledrive_2.1.1 locfit_1.5-9.10 graphlayouts_1.1.1 bit_4.0.5
## [93] fastmatch_1.1-4 codetools_0.2-20 crosstalk_1.2.1 bslib_0.8.0
## [97] SeuratData_0.2.2.9001 plotly_4.10.4 mime_0.12 splines_4.4.1
## [101] Rcpp_1.0.13 fastDummies_1.7.3 sparseMatrixStats_1.16.0 HDO.db_0.99.1
## [105] cellranger_1.1.0 knitr_1.48 blob_1.2.4 utf8_1.2.4
## [109] here_1.0.1 seqLogo_1.70.0 AnnotationFilter_1.28.0 fs_1.6.4
## [113] listenv_0.9.1 DelayedMatrixStats_1.26.0 tibble_3.2.1 Matrix_1.7-0
## [117] statmod_1.5.0 tzdb_0.4.0 tweenr_2.0.3 pkgconfig_2.0.3
## [121] tools_4.4.1 cachem_1.1.0 RSQLite_2.3.7 DBI_1.2.3
## [125] fastmap_1.2.0 rmarkdown_2.27 scales_1.3.0 grid_4.4.1
## [129] ica_1.0-3 shinydashboard_0.7.2 Rsamtools_2.20.0 sass_0.4.9
## [133] BiocManager_1.30.23 dotCall64_1.1-1 RANN_2.6.1 farver_2.1.2
## [137] scatterpie_0.2.3 tidygraph_1.3.1 yaml_2.3.10 rtracklayer_1.64.0
## [141] cli_3.6.3 purrr_1.0.2 leiden_0.4.3.1 lifecycle_1.0.4
## [145] uwot_0.2.2 presto_1.0.0 BSgenome.Hsapiens.UCSC.hg38_1.4.5 BiocParallel_1.38.0
## [149] annotate_1.82.0 gtable_0.3.5 rjson_0.2.21 ggridges_0.5.6
## [153] progressr_0.14.0 ape_5.8 parallel_4.4.1 jsonlite_1.8.8
## [157] RcppHNSW_0.6.0 TFBSTools_1.42.0 bitops_1.0-8 bit64_4.0.5
## [161] Rtsne_0.17 yulab.utils_0.1.5 spatstat.utils_3.0-5 CNEr_1.40.0
## [165] highr_0.11 jquerylib_0.1.4 GOSemSim_2.30.0 shinyjs_2.1.0
## [169] SeuratDisk_0.0.0.9021 spatstat.univar_3.0-0 R.utils_2.12.3 lazyeval_0.2.2
## [173] shiny_1.9.1 htmltools_0.5.8.1 enrichplot_1.24.2 GO.db_3.19.1
## [177] sctransform_0.4.1 rappdirs_0.3.3 ensembldb_2.28.0 glue_1.7.0
## [181] TFMPvalue_0.0.9 spam_2.10-0 googlesheets4_1.1.1 XVector_0.44.0
## [185] RCurl_1.98-1.16 treeio_1.28.0 rprojroot_2.0.4 BSgenome_1.72.0
## [189] gridExtra_2.3 JASPAR2020_0.99.10 igraph_2.0.3 R6_2.5.1
## [193] labeling_0.4.3 forcats_1.0.0 RcppRoll_0.3.1 GenomicFeatures_1.56.0
## [197] cluster_2.1.6 Rhdf5lib_1.26.0 gargle_1.5.2 aplot_0.2.3
## [201] DirichletMultinomial_1.46.0 DelayedArray_0.30.1 tidyselect_1.2.1 ProtGenerics_1.36.0
## [205] ggforce_0.4.2 AnnotationDbi_1.66.0 future_1.34.0 munsell_0.5.1
## [209] KernSmooth_2.23-24 htmlwidgets_1.6.4 fgsea_1.30.0 RColorBrewer_1.1-3
## [213] rlang_1.1.4 spatstat.sparse_3.1-0 spatstat.explore_3.3-1 fansi_1.0.6